Signal processors are typically employed to remove undesired portions from a composite measured signal including a desired signal portion and an undesired signal portion. If the undesired signal portion occupies a different frequency spectrum than the desired signal, then conventional filtering techniques such as low pass, band pass, and high pass filtering could be used to separate the desired portion from the total signal. Fixed single or multiple notch filters could also be employed if the undesired signal portion(s) exist at a fixed frequency(s).
However, it is often the case that an overlap in frequency spectrum between the desired and undesired signal portions does exits and the statistical properties of both signal portions change with time. In such cases, conventional filtering techniques are totally ineffective in extracting the desired signal. If, however, a description of the undesired portion can be made available, adaptive noise canceling can be employed to remove the undesired portion of the signal leaving the desired portion available for measurement. Adaptive noise cancelers dynamically change their transfer function to adapt to and remove the undesired signal portions of a composite signal. Adaptive noise cancelers require a noise reference signal which is correlated to the undesired signal portion. The noise reference signal is not necessarily a representation of the undesired signal portion, but has a frequency spectrum which is similar to that of the undesired signal. In many cases, it requires considerable ingenuity to determine a noise reference signal since nothing is a priori known about the undesired signal portion.
One area where composite measured signals comprise a desired signal portion and an undesired signal portion about which no information can easily be determined is physiological monitoring. Physiological monitoring apparatuses generally measure signals derived from a physiological system, such as the human body. Measurements which are typically taken with physiological monitoring systems include electron cardiographs, blood pressure, blood gas saturation (such as oxygen saturation), capnographs, heart rate, respiration rate, and depth of anesthesia, for example. Other types of measurements include those which measure the pressure and quantity of a substance within the body such as breathalizer testing, drug testing, cholesterol testing, glucose testing, arterial carbon dioxide testing, protein testing, and carbon monoxide testing, for example. The source of the undesired signal portion in these measurements is often due to motion of the patient, both external and internal (muscle movement, for example), during the measurement process.
Knowledge of physiological systems, such as the amount of oxygen in a patient's blood, can be critical, for example during surgery. Data can be determined by a lengthy invasive procedure of extracting and testing matter, such as blood, from a patient, or by more expedient, non-invasive measures. Many types of non-invasive measurements can be made by using the known properties of energy attenuation as a selected form of energy passes through a medium.
Energy is caused to be incident on a medium either derived from or contained within a patient and the amplitude of transmitted or reflected energy is then measured. The amount of attenuation of the incident energy caused by the medium is strongly dependent on the thickness and composition of the medium through which the energy must pass as well as the specific form of energy selected. Information about a physiological system can be derived from data taken from the attenuated signal of the incident energy transmitted through the medium if the noise can be removed. However, non-invasive measurements often do not afford the opportunity to selectively observe the interference causing the undesired signal portion, making it difficult to remove.
These undesired signal portions often originate from both AC and DC sources. The first undesired portion is an easily removed DC component caused by transmission of the energy through differing media which are of relatively constant thickness within the body, such as bone, tissue, skin, blood, etc. Second, is an erratic AC component caused when differing media being measured are perturbed and thus, change in thickness while the measurement is being made. Since most materials in and derived from the body are easily compressed, the thickness of such matter changes if the patient moves during a non-invasive physiological measurement. Patient movement can cause the properties of energy attenuation to vary erratically. Traditional signal filtering techniques are frequently totally ineffective and grossly deficient in removing these motion induced effects from a signal. The erratic or unpredictable nature of motion induced undesired signal components is the major obstacle in removing them. Thus, presently available physiological monitors generally become totally inoperative during time periods when the patient moves.
A blood gas monitor is one example of a physiological monitoring system which is based upon the measurement of energy attenuated by biological tissues or substances. Blood gas monitors transmit light into the tissue and measure the attenuation of the light as a function of time. The output signal of a blood gas monitor which is sensitive to the arterial blood flow contains a component which is a waveform representative of the patient's arterial pulse. This type of signal, which contains a component related to the patient's pulse, is called a plethysmographic wave, and is shown in FIG. 1 as curve Y. Plethysmographic waveforms are used in blood pressure or blood gas saturation measurements, for example. As the heart beats the amount of blood in the arteries increases and decreases, causing increases and decreases in energy attenuation, illustrated by the cyclic wave Y in FIG. 1.
Typically, a digit such as a finger, an ear lobe, or other portion of the body where blood flows close to the skin, is employed as the medium through which light energy is transmitted for blood gas attenuation measurements. The finger comprises skin, fat, bone, muscle, etc., shown schematically in FIG. 2, each of which attenuates energy incident on the finger in a generally predictable and constant manner. However, when fleshy portions of the finger are compressed erratically, for example by motion of the finger, energy attenuation becomes erratic.
An example of a more realistic measured waveform S is shown in FIG. 3, illustrating the effect of motion. The desired portion of the signal Y is the waveform representative of the pulse, corresponding to the sawtooth-like pattern wave in FIG. 1. The large, motion-induced excursions in signal amplitude hide the desired signal Y. It is easy to see how even small variations in amplitude make it difficult to distinguish the desired signal Y in the presence of a noise component n.
A specific example of a blood gas monitoring apparatus is a pulse oximeter which measures the saturation of oxygen in the blood. The pumping of the heart forces freshly oxygenated blood into the arteries causing greater energy attenuation. The saturation of oxygenated blood may be determined from the depth of the valleys relative to the peaks of two plethysmographic waveforms measured at separate wavelengths. However, motion induced undesired signal portions, or motion artifacts, must be removed from the measured signal for the oximeter to continue the measurement during periods when the patient moves.